pseudonymized

Pseudonymization is a data processing technique in which direct identifiers, such as names or social security numbers, are replaced with substitutes called pseudonyms or tokens. The mapping between the pseudonym and the real identity is kept separately and protected to prevent easy re-linking. This approach aims to reduce the risk of identification while preserving enough data to enable analysis and processing.

Under privacy law such as the European Union’s GDPR, pseudonymized data is still considered personal data because it can be re-identified with the right information. This differs from anonymization, where identifiers are removed to the point that re-identification is not reasonably possible. Pseudonymization is commonly viewed as a risk-reduction measure and a technical safeguard that can facilitate data processing for research and analytics while helping to meet data protection obligations.

Common methods include tokenization, hashing with a secret salt, and reversible encryption. Tokenization replaces identifiers with tokens that require a separate mapping to restore the original value. Hashing with a salt can impede straightforward reversal, while reversible encryption uses a key to retrieve the original identifiers when needed. Effective implementation requires protecting the re-identification key separately, enforcing strict access controls, and maintaining audit trails.

Applications span healthcare data sharing, customer analytics, and machine learning, where reducing identifiability helps manage privacy risks. Standards and guidance from privacy frameworks emphasize pseudonymization as a security measure, while acknowledging that it does not remove all data protection obligations.